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This study draws a comparison between the Global Financial Crisis (GFC) and the COVID-19 pandemic crisis to assess the safehaven potential of Islamic stocks for G7 stock markets. We employ the cross-quantilogram framework of Han et al., which considers the non-linearity in the relationship, and thus captures the correlation between the Islamic and G7 stock markets across various quantiles reflecting different market conditions. The analysis also includes the time-varying cross-quantile correlation to observe the evolution of Islamic stocks' safe-haven potential. Our full sample analysis shows that Islamic stocks do not exhibit safe-haven properties for G7 stock markets. During the GFC period, Islamic stocks show some diversification benefits for the G7 stock markets. Notably, Islamic stocks emerged as a robust safe-haven asset for the G7 stock markets during the pandemic crisis. The study carries essential insights for equity investors and regulators of G7 and other countries to implement diversification/hedging strategies that would involve Islamic stocks to protect equity investments and the overall financial system amid the financial downturns.

Focusing on raising climate concerns and sustaining a clean ecosystem, the current study strives to examine the connectedness of clean energy markets with conventional energy markets and four regional stock markets of Asia, Pacific, Europe, and America for the period spanning January 1, 2004 to August 31, 2021. We employed the volatility connectedness methodology using dynamic conditional correlation (DCC-GARCH) estimates for analysis purposes. There is pronounced within class connectedness of all markets except conventional energy markets, which showed strong disconnection from the network. However, strong inter-class spillovers are reported between clean energy and regional stock markets. Time-varying analysis revealed that intense spillovers are shaped during the Global Financial Crisis, Shale Oil Crisis, and COVID-19 pandemic. Meanwhile, time-varying net connectedness estimates illuminate that world renewable energy and American stock markets are net transmitters, whereas leftover markets are net recipients of spillovers. Further analysis of sub-sample periods during GFC, SOR, and COVID-19 validate that intense spillovers are formed when markets experience unexpected financial, economic, and global health turmoil. We proposed significant implications for regional stock markets of Asia, Pacific, Europe and America to concentrate on the climate-friendly energy markets than conventional energy markets as they service the clean ecosystem motives more specifically.

Since markets are undergoing severe turbulent economic periods, this study investigates the information transmission of energy stock markets of five regions including North America, South America, Europe, Asia, and Pacific where we differentiated the regional energy markets based on their developing and developed state of economy. We employed time–frequency domain from Jan 1995 to May 2021 and found that energy stocks of developed regions are highly connected. The energy markets of North America, South America, and Europe are the net transmitters of spillovers, whereas the Asian and Pacific energy markets are the net receivers of spillovers. The results also reveal that the connectedness of regional energy markets is time and frequency dependent. Regional energy stocks were highly connected following the Asian financial crisis (AFC), global financial crisis (GFC), European debt crisis (EDC), shale oil revolution (SOR), and COVID-19 pandemic. Time-dependent results reveal that high spillovers formed during stress periods and frequency domain show the higher connectedness of regional energy stock markets in the short run followed by an extreme economic condition. These results have significant implications for policymakers, regulators, investors, and regional controlling bodies to adopt effective strategies during short run to avoid economic downturns and information distortions.

Nowadays, artificial intelligent (AI) is becoming a more effective digital domain promised to facilitate immediate access to information and effective decision making in ever-increasing business environments. The researchers understand the extensive use of artificial intelligence among firms as an essential and necessary tool for shaping the future of supply chain 4.0 industry. This chapter discusses the role of AI applications for the success of a supply chain in the big data era. From a holistic perspective, today, manufacturers, particularly those with global operations and presence, are under enormous pressure to keep up with the continuous growth of disruptive innovative procurement models. This has open doors for the firms to aggressively seek out big data management capabilities to improve operational efficiencies and to innovate the process. This chapter provides a better understanding related to the application of data analytics in the supply chain context. The research issues are classified into different categories, including big data management and machine learning, a business case for the supply chain and innovation in supply using data. This study also present machine learning data analysis steps.

Corporate governance plays a significant role in the value of shareholders and share prices, hence stock market liquidity is affected. Previous research has mainly focused on the issue in developed markets, whereas in developing countries there is a need to analyze the influence of corporate governance on stock market liquidity. Therefore, the present study aims to examine the impact of ownership structure and board characteristics on stock market liquidity of non-financial firms of South Asian countries such as Pakistan, Sri Lanka, Bangladesh, and India. The data in the study is collected from the DataStream for the 2011–2020 period. The study uses a fixed effect model for the analysis of the data and hypotheses testing and generalized method of moments (GMM) is used to check the robustness of the results. The findings of the study indicate that institutional ownership, board size, board independence, and CEO duality have a significant and positive impact on stock market liquidity, whereas managerial ownership has a significant and negative effect on stock market liquidity.

We aim to construct portfolios by employing different risk models and compare their performance in order to understand their appropriateness for effective portfolio management for investors. Mean variance (MV), semi variance (SV), mean absolute deviation (MaD) and conditional value at risk (CVaR) are considered as risk measures. The price data were extracted from the Pakistan stock exchange, Bombay stock exchange and Dhaka stock exchange under diverse economic conditions such as crisis, recovery and growth. We take the average of GDP of the selected period of each country as a cut-off point to make three economic scenarios. We use 40 stocks from the Pakistan stock exchange, 92 stocks from the Bombay stock exchange and 30 stocks from the Dhaka stock exchange. We compute optimal weights using global minimum variance portfolio (GMVP) for all stocks to construct optimal portfolios and analyze the data by using MV, SV, MaD and CVaR models for each subperiod. We find that CVaR (95%) gives better results in each scenario for all three countries and performance of portfolios is inconsistent in different scenarios.

The regulators' role Ownership concentration Foreign ownership Capital requirements Return on asset (ROA) Return on equity (ROE).